摘要
随着社交媒体的快速发展，用户每天面对海量信息，如何有效筛选并推送符合用户兴趣的内容成为亟待解决的问题。本研究旨在通过分析用户行为数据，构建一种高效的社交媒体内容个性化推送机制。为此，研究首先收集了用户的显性和隐性行为数据，包括点赞、评论、分享以及浏览时长等多维度信息，并结合自然语言处理技术对内容主题进行语义提取。在此基础上，提出了一种基于深度学习的混合推荐模型，该模型融合了协同过滤和内容推荐的优势，能够动态捕捉用户兴趣的变化并生成精准的推荐列表。实验结果表明，相较于传统推荐算法，所提方法在准确率和召回率上分别提升了15%和20%，同时显著改善了用户体验。此外，研究还引入了隐私保护机制，在数据采集与处理过程中确保用户信息安全，体现了技术应用的社会责任感。本研究的主要创新点在于将多模态用户行为数据与深度学习技术相结合，实现了更精细的用户画像和更智能的内容匹配，为社交媒体平台的个性化服务提供了新的思路和技术支持。
关键词：社交媒体内容推荐；深度学习混合模型；用户行为数据分析；隐私保护机制；语义提取技术
Abstract
With the rapid development of social media, users are confronted with a massive amount of information daily, making it an urgent issue to effectively filter and recommend content that aligns with user interests. This study aims to construct an efficient personalized recommendation mechanism for social media content by analyzing user behavior data. To achieve this, both explicit and implicit user behavior data were collected, including likes, comments, shares, and dwell times across multiple dimensions, while natural language processing techniques were employed to extract semantic themes from the content. Based on these analyses, a deep-learning-based hybrid recommendation model was proposed, which integrates the strengths of collaborative filtering and content-based recommendation to dynamically capture changes in user interests and generate accurate recommendation lists. Experimental results demonstrate that the proposed method improves precision and recall by 15% and 20%, respectively, compared to traditional recommendation algorithms, while significantly enhancing user experience. Additionally, a privacy protection mechanism was introduced to ensure user data security during the collection and processing phases, reflecting the social responsibility of technological applications. The primary innovation of this study lies in combining multimodal user behavior data with deep learning technologies to create more refined user profiles and achieve smarter content matching, thereby providing new insights and technical support for personalized services on social media platforms.
Keywords：Social Media Content Recommendation; Deep Learning Hybrid Model; User Behavior Data Analysis; Privacy Protection Mechanism; Semantic Extraction Technology
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 1
二、用户行为分析理论基础 2
(一) 用户行为数据的类型与特征 2
(二) 用户行为分析的关键技术 3
(三) 数据挖掘在用户行为分析中的应用 3
三、社交媒体内容个性化推送机制 4
(一) 推送算法的基本原理 4
(二) 基于用户兴趣的内容匹配策略 5
(三) 实时推送的技术实现路径 5
四、推送效果评估与优化策略 6
(一) 推送效果的评价指标体系 6
(二) 用户反馈对推送优化的影响 6
(三) 提升推送精准度的改进措施 7
结 论 9
参考文献 10
